WiNELL: Wikipedia Never-Ending Updating with LLM Agents

ACL ARR 2025 May Submission5244 Authors

Abstract:

Wikipedia, a vast and continuously consulted knowledge base, faces significant challenges in maintaining up-to-date content due to its reliance on manual human editors. Inspired by the vision of continuous knowledge acquisition in NELL and fueled by advances in LLM-based agents, this paper introduces WiNELL, an agentic framework for continuously updating Wikipedia articles. Our approach employs a multi-agent framework to aggregate online information, select new and important knowledge for a target entity in Wikipedia, and then generate precise edit suggestions for human review. Our fine-grained editing models, trained on Wikipedia's extensive history of human edits, enable incorporating updates in a manner consistent with human editing behavior. Our editor models outperform both open-source instruction-following baselines and closed-source LLMs (e.g., GPT-4o) in key-information coverage and editing efficiency.
End-to-end evaluation on high-activity Wikipedia pages demonstrates WiNELL's ability to identify and suggest timely factual updates. This opens up a promising research direction in LLM agents for automatically updating knowledge bases in a never-ending fashion.

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: Information Aggregation, LLM Agents, Automatic Updating

Contribution Types: NLP engineering experiment

Languages Studied: English

Keywords: Information Aggregation, LLM Agents, Automatic Updating

Submission Number: 5244